Abstract
In this chapter, we first look at patterns with their relevance of discovery to business. We then do a survey and evaluation, in terms of advantages and disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining for both the sequential and parallel processing environments.
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Appendix: Summary on Mining Algorithms
Appendix: Summary on Mining Algorithms
Author | Algorithm | Mining approach | Approach used | Advantages | Limitations |
|---|---|---|---|---|---|
Aggarwal and Han (2014) | Apriori-based methods | However, a candidate-generation-and-test strategy produces a large number of candidate sequences and also requires more database scan (Tu and Koh 2010) when there are long patterns | |||
Han et al. (2000) | Pattern-growth methods | Without candidate generation compressed database structure which is smaller than original dataset | |||
Han et al. (2000) | FreeSpan | Pattern-growth methods | sequential patterns by partitioning | ||
Pei et al. (2001) | PrefixSpan | Pattern-growth methods | Pseudo-projection technique for constructing projected databases | Does not generate-and-test any candidate sequence that do not exist in a projected database | Projected database requires more storage space, extra time is required to scan the projected database |
Sequential pattern mining | Vertical format-based methods | Fast computation of support counting | |||
Zaki (2001) | SPADE | Sequential pattern mining | Vertical format-based methods Either breadth-first or depth-first manner | Consumes more memory | |
Ayres et al. (2002) | SPAM | Sequential pattern mining | Vertical format-based methods Traverses the sequence tree in a depth-first manner | A vertical bitmap of the database | Consumes more memory space |
Agrawal and Srikant (1995) | AprioriAll | Sequential pattern mining | Horizontal format-based method | Consumes more memory space | |
Closed sequential pattern mining | Efficient use of search space pruning, reduced number of pattern, find more interesting patterns | ||||
Yan et al. (2003) | CloSpan | Closed sequential pattern mining | Prefix sequence lattice, post-pruning | Huge search space for checking the closure of new patterns | |
Wang et al. (2007) | BIDE | Closed sequential pattern mining | Depth-first search order. perform closure checking (bidirectional Extension) | Without candidate maintenance, does not keep track of historical closed sequential patterns | Multiple database scan, more computational time |
Huang et al. (2006) | COBRA | Closed sequential pattern mining | Bi-phase Reduction Approach, item encoding, pruning methods (LayerPruning and ExtPruning), vertical and horizontal database formats | Reduce searching space | Requires large memory space |
Raju and Varma (2015 | ClaSP | Closed sequential pattern mining | Vertical database format, Frequent Closed Candidates, recursive post-pruning (CheckAvoidable for pruning the search) | Requires more main memory | |
Han et al. (2002) | Top-K closed sequential pattern mining | Descending order of support | Without specifying minimum support | Users must decide the value of k, prior knowledge of database required | |
Hirate et al. (2004) | TF2P-growth | Top-K closed sequential pattern mining | Descending order of support | Does not require the user to set any threshold value k, output of frequent patterns to user sequentially and in chunks | Time-consuming to be checking all chunk size |
Wang et al. (2014) | BI-TSP | Top-K closed sequential pattern mining | bidirectional checking scheme, minimum length constraint, dynamically increase support of k | ||
Raju and Varma (2015) | CSpan | Top-K closed sequential pattern mining | Depth-first search, occurrence checking method for early detection of closed sequential patterns, constructs the projected database | Projected database requires more storage space, extra time is required to scan the projected database |
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Millham, R., Agbehadji, I.E., Yang, H. (2021). Pattern Mining Algorithms. In: Fong, S., Millham, R. (eds) Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-6695-0_4
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